


'''simple docstring'''




import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class 			_lowercase (	__lowercase						,					__lowercase						,					__lowercase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Dict          =	StableUnCLIPPipeline
   _SCREAMING_SNAKE_CASE  :  Tuple          =	TEXT_TO_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE  :  Any          =	TEXT_TO_IMAGE_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE  :  Dict          =	TEXT_TO_IMAGE_IMAGE_PARAMS
   _SCREAMING_SNAKE_CASE  :  Any          =	TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	False
   def a  (						self       :      Tuple      ) ->							Any:
        __snake_case           =						32
        __snake_case           =						embedder_hidden_size

        # prior components

        torch.manual_seed(0      )
        __snake_case           =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

        torch.manual_seed(0      )
        __snake_case           =						CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=SCREAMING_SNAKE_CASE_       ,   projection_dim=SCREAMING_SNAKE_CASE_       ,   intermediate_size=37       ,   layer_norm_eps=1e-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1000       ,   )      )

        torch.manual_seed(0      )
        __snake_case           =						PriorTransformer(
            num_attention_heads=2       ,   attention_head_dim=12       ,   embedding_dim=SCREAMING_SNAKE_CASE_       ,   num_layers=1       ,   )

        torch.manual_seed(0      )
        __snake_case           =						DDPMScheduler(
            variance_type='fixed_small_log'       ,   prediction_type='sample'       ,   num_train_timesteps=1000       ,   clip_sample=SCREAMING_SNAKE_CASE_       ,   clip_sample_range=5.0       ,   beta_schedule='squaredcos_cap_v2'       ,   )

        # regular denoising components

        torch.manual_seed(0      )
        __snake_case           =						StableUnCLIPImageNormalizer(embedding_dim=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						DDPMScheduler(beta_schedule='squaredcos_cap_v2'      )

        torch.manual_seed(0      )
        __snake_case           =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

        torch.manual_seed(0      )
        __snake_case           =						CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=SCREAMING_SNAKE_CASE_       ,   projection_dim=32       ,   intermediate_size=37       ,   layer_norm_eps=1e-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1000       ,   )      )

        torch.manual_seed(0      )
        __snake_case           =						UNetaDConditionModel(
            sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,   up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,   block_out_channels=(32, 64)       ,   attention_head_dim=(2, 4)       ,   class_embed_type='projection'       ,   projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,   cross_attention_dim=SCREAMING_SNAKE_CASE_       ,   layers_per_block=1       ,   upcast_attention=SCREAMING_SNAKE_CASE_       ,   use_linear_projection=SCREAMING_SNAKE_CASE_       ,   )

        torch.manual_seed(0      )
        __snake_case           =						DDIMScheduler(
            beta_schedule='scaled_linear'       ,   beta_start=0.0_0_0_8_5       ,   beta_end=0.0_1_2       ,   prediction_type='v_prediction'       ,   set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,   steps_offset=1       ,   )

        torch.manual_seed(0      )
        __snake_case           =						AutoencoderKL()

        __snake_case           =						{
            # prior components
            'prior_tokenizer': prior_tokenizer,
            'prior_text_encoder': prior_text_encoder,
            'prior': prior,
            'prior_scheduler': prior_scheduler,
            # image noising components
            'image_normalizer': image_normalizer,
            'image_noising_scheduler': image_noising_scheduler,
            # regular denoising components
            'tokenizer': tokenizer,
            'text_encoder': text_encoder,
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
        }

        return components
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=0      ) ->							Optional[Any]:
        if str(SCREAMING_SNAKE_CASE_      ).startswith('mps'      ):
             __snake_case           =						torch.manual_seed(SCREAMING_SNAKE_CASE_      )
        else:
             __snake_case           =						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						{
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'prior_num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs
   def a  (						self       :      int      ) ->							str:
        __snake_case           =						torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=SCREAMING_SNAKE_CASE_      )





   def a  (						self       :      Optional[Any]      ) ->							List[Any]:
        __snake_case           =						torch_device in ['cpu', 'mps']

        self._test_inference_batch_single_identical(test_max_difference=SCREAMING_SNAKE_CASE_      )




@slow
@require_torch_gpu
class 			_lowercase (	unittest.TestCase							):
   def a  (						self       :      Any      ) ->							Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'      )

        __snake_case           =						StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,   torch_dtype=torch.floataa      )
        pipe.to(SCREAMING_SNAKE_CASE_      )
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __snake_case           =						torch.Generator(device='cpu'      ).manual_seed(0      )
        __snake_case           =						pipe('anime turle'       ,   generator=SCREAMING_SNAKE_CASE_       ,   output_type='np'      )

        __snake_case           =						output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )





   def a  (						self       :      Optional[int]      ) ->							Optional[int]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __snake_case           =						StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,   torch_dtype=torch.floataa      )
        __snake_case           =						pipe.to(SCREAMING_SNAKE_CASE_      )
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __snake_case           =						pipe(
            'anime turtle'       ,   prior_num_inference_steps=2       ,   num_inference_steps=2       ,   output_type='np'       ,   )

        __snake_case           =						torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


